What type of study is this?

This is a Literature Review study.

September 10, 2025Open Access

Examining Non-Significant Results with Bayes Factors and Equivalence Tests

View Full Paper

Ask AI

Bookmark

Equivalence tests provide an alternative interpretation of non-significant results, offering unique insights.
Bayes factors alone may not sufficiently convey the nuances of non-significant effects in research.
Using both bayes factors and equivalence tests enhances the reporting and understanding of research outcomes.
The analysis highlights advantages of equivalence testing over bayes factors in certain contexts.

Implication

The blog compares bayes factors and equivalence tests to interpret non-significant results, suggesting the latter may be more beneficial.

Abstract

In this blog, I’ll compare two ways of interpreting non-significant effects: Bayes factors and TOST equivalence tests. I’ll explain why reporting more than only Bayes factors makes sense, and highlight some benefits of equivalence testing over Bayes factors.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Daniël Lakens (2017) studied this question.

synapsesocial.com/papers/68c1e17854b1d3bfb60feea2 https://doi.org/10.59350/sydm9-ec945

View Full Paper

Ask AI

Bookmark

Implication

The blog compares bayes factors and equivalence tests to interpret non-significant results, suggesting the latter may be more beneficial.

Examining Non-Significant Results with Bayes Factors and Equivalence Tests

Key Points

Implication

Abstract

Discussion

Cite This Study

Examining Non-Significant Results with Bayes Factors and Equivalence Tests

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading